We conducted experiments considering different domain shifts and validating our approach on various vehicle counting datasets. First, we employed two existing datasets for traffic density estimation, WebCamT [1]} and TRANCOS [2]}. We used images acquired by a specific subset of cameras as the source domain. In contrast, to emphasize the domain shift problem, we represented the target domain with images captured by a different subset of cameras, seeing different perspectives and visual contexts. We called this type of domain shift Camera2Camera domain shift. Comparisons with other techniques on these datasets showed the superiority of our approach. To test our solution with other types of domain shifts, we created and made publicly available two additional datasets, described in the following. The first one was the ndispark dataset, consisting of images captured by surveillance cameras in parking lots. Here, on the one hand, source data include annotated images collected by various cameras during the day. On the other hand, the unlabeled target domain contains images collected during the night in the same scenarios. We called this domain shift Day2Night. The second one was instead the gta dataset, a vast collection of synthetic images generated with the highly photo-realistic graphical engine of the Grand Theft Auto V video game, developed by Rockstar North. This dataset consists of urban traffic scenes, automatically and precisely annotated with per-pixel annotations. To the best of our knowledge, this was the first instance segmented synthetic dataset of traffic scenarios. We used this dataset to train the counting algorithm. Then, we performed uda to be able to count in real-world images. In this case, the domain shift is represented by the Synthetic2Real difference. Different from the previous chapter, in which we relied on a supervised adaptation of the network using real-world data, here we dispensed the usage of the annotations of the target data. Using the proposed uda strategy, we tackled the problem of data scarcity from two complementary sides: on the one hand, we exploited the great variability of the synthetic data, while, on the other hand, we mitigated the domain gap existing between the synthetic and the real-world data in an unsupervised fashion. examplesdomaingap summarizes the described domain shifts that we have addressed. In all the experiments, we showed that our uda technique always outperformed the non-domain adapted models.
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